Roy Lab

A computational biology group interested in developing statistical computational methods to understand regulatory networks driving cellular functions. The lab works to identify networks under different environmental, developmental and evolutionary contexts, comparing these networks across contexts, and construct predictive models from these networks.

Akhil Sundararajan

  • Graduate Student

Convergence analysis of convex optimization algorithms using techniques from robust control theory

Blake Mason

  • Postdoctoral Associate

Metric learning and active machine learning, focussed on reducing the sample complexity of learning.

Brittany Baur

Brittany Baur

  • Postdoctoral Associate

Predicting long-range enhancer-promoter interactions

Junha Shin

Junha Shin

  • Postdoctoral Associate

Bioinformatics based on networks and large-scale quantitative genomics.

Lauren Michael

Lauren Michael

Research Computing Facilitator, Center for High Throughput Computing

  • Discovery Fellow

Facilitating compute-driven transformations to research projects via personalized consulting.

ZhengZheng (Jane) Tang

ZhengZheng Tang

Assistant Professor

  • Discovery Fellow

Developing statistical methods and computational tools for high-throughput omics data.

Stephen Wright

Steve Wright

George B. Dantzig Professor of Computer Sciences

  • Discovery Fellow

Optimization algorithms with applications to data analysis and other areas.

Sushmita Roy

Sushmita Roy

Associate Professor

  • WID Faculty

Computational methods to model cellular networks

Michael Ferris

Michael Ferris

Jacques-Louis Lions Chair and Data Science Hub Leader

  • WID Faculty

Optimization methods and data modeling for large scale problems in science, engineering and economics

Inference and Evolutionary Analysis of Genome-Scale Regulatory Networks in Large Phylogenies

In a paper in Cell Systems, Sushmita Roy and colleagues develop a probabilistic graphical model-based method, multi-species regulatory network learning that uses a phylogenetic framework to infer regulatory networks in multiple species simultaneously.

Understanding the Immune System with Machine Learning

Systems Biology researchers Deborah Chasman and Sushmita Roy are using machine learning to identify virus and pathogenicity-specific regulatory networks which may guide the design of effective therapeutics for infectious diseases. The work is described in a recent paper in PLOS Computational Biology.